Description Usage Arguments Details Value Note Author(s) See Also Examples
View source: R/SnpSetIlluminaGenotypes.R
Heterozygous SNPs are determined based on quality score criteria
1 2 | heterozygousSNPs(object, threshold=0.9, useQuality=TRUE, relative=TRUE,
percentile=FALSE)
|
object |
class SnpSetIllumina |
threshold |
numeric (0:1) minimum quality score to be called heterozygous |
useQuality |
logical, use quality score |
relative |
logical, use quality score relative to GTS, see details |
percentile |
logical, use percentage of probes above threshold |
This function presumes that the specificity for determining heterozygity is
more important than the sensitivity, and will therefore only call probes heterozygous
if that can be done with high certainty.
The Illumina genotyping software calculates two quality measures: gen train score (GTS)
and gen call score (GCS). The GTS is a measure for how well clusters can be recognized
in a training set. This value is probe specific, and the same for all samples in an
experiment. The GCS is a probe-specific, sample specific value that measures how close
a probe in a sample is to the clusters determined in the training step. This value is
always lower than the GTS for a probe.
read.SnpSetIllumina
will put GCS into the callProbability
element
of the assaydata
slot and the GTS into the featureData
slot. The
function uses these locations to retrieve the necessary information.
If relative
is FALSE
then the raw GCS values are compared to the
threshold
. In this case a threshold
of around 0.5 should be used. If
relative
is TRUE
then GCS/GTS is compared to the threshold
and
threshold
should be around 0.9.
With percentile=TRUE
the threshold
quantile is calculated for each sample,
and only probes with higher scores can be called heterozygous. A threshold
of around 0.2 seems to work fine usually.
This function returns a logical matrix
with same dimensions as object
.
The purpose of the function is to separate heterozygous probes from non-heterozygous probes. In tumor samples the determination of the genotype can be difficult, because of aneuploidy and the fact that a sample is often a mixture of normal and tumor cells.
Jan Oosting
1 2 3 | data(chr17.260)
plot(heterozygosity(heterozygousSNPs(chr17.260[,"514TV"])),col="red",pch="x")
points(heterozygosity(exprs(chr17.260)[,"514TV"]))
|
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